2020/04/06 by Alexandru Popa, Popa, Alexandru
Computer Science · Mathematics · #COVID-19 epidemiological studies #FOS: Computer and information sciences #Social and Information Networks (cs.SI) #cs.SI
paper · pdf · doi:10.48550/arxiv.2004.02807
14 pages, 6 figures
arxiv created 2020/04/06 · openalex publication_date 2020/04/06 · arxiv updated 2020/04/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Currently there are many attempts around the world to use computers, smartphones, tablets and other electronic devices in order to stop the spread of COVID-19. Most of these attempts focus on collecting information about infected people, in order to help healthy people avoid contact with them. However, social distancing decisions are still taken by the governments empirically. That is, the authorities do not have an automated tool to recommend which decisions to make in order to maximize social distancing and to minimize the impact for the economy. In this paper we address the aforementioned problem and we design an algorithm that provides social distancing methods (i.e., what schools, shops, factories, etc. to close) that are efficient (i.e., that help reduce the spread of the virus) and have low impact on the economy. On short: a) we propose several models (i.e., combinatorial optimization problems); b) we show some theoretical results regarding the computational complexity of the formulated problems; c) we give an algorithm for the most complex of the previously formulated problems; d) we implement and test our algorithm; and e) we show an integer linear program formulation for our problem.